Carcinomas are the most common form of cancer, and are responsible for the majority of cancer-related deaths worldwide. Early detection of cancer significantly improves a prognosis, as evidenced by the 70% reduction in mortality in cervical cancer that was observed after the Papanicolaou test became accepted as a routine annual examination in the United States. Likewise, mortality rates from breast cancer have been reduced by up to 30% because of earlier detection through manual examination and mammograms. Unfortunately, the relative inaccessibility of most body tissues currently limits the breadth of cancer screening. Even when tumors are detected by existing techniques and removed surgically, there is a strong inverse correlation between tumor size and out-come, such that cancer survival rates are higher when tumors are detected early and removed while the tumors are relatively small in size.
The analysis of accessible body fluids for the detection of neoplastic cells should greatly facilitate earlier cancer detection, and the detection of micro-metastases and/or cells originating from a solid tumor in body fluids of patients who have early stage cancer could have a substantial impact on optimizing therapeutic regimens and, thus, long-term prognosis. Unfortunately, even when cancer is present in a patient, the relative number of cancer cells in readily accessible bodily fluids, such as blood, can be on the order of one cell per milliliter of fluid, making cancer detection by sampling bodily fluids very challenging. Classic manual microscopy-based analysis, although the gold standard in diagnostics, lacks the throughput required to identify rare cell populations consistently and with confidence, because the time required for manual review of millions of cells in a blood sample is simply too great to be practical. Flow cytometry offers much higher data acquisition and sample processing rates, but flow cytometry depends largely on the availability of fluorescently labeled markers to discriminate between normal cells and neoplastic cells. This requirement presents a challenge, since the tumor-specific markers may not be known ahead of time and even when they are, the markers expressed by circulating tumor cells can differ from those expressed within the tumor of origin.
The use of an antibody-based approach to address this problem depends on ectopic expression of a normal antigenic epitope, formation of a new epitope through genetic mutation or recombination, or consistent modulation of the expression of a marker expressed in transformed and non-transformed cells. Further, the cost of antibodies for use in detecting cancerous cells may be prohibitive in a screening context. The approach is confounded further by the diversity of neoplastic transformations and genetic heterogeneity in the human population.
In contrast to single- or multi-parameter antibody-based techniques, cellular morphology analysis is a further effective means of cancer screening. For instance, dysplastic and neoplastic cells can be detected in lung sputum on the basis of morphology. Likewise, exfoliated cells collected from bladder washings of bladder cancer patients have been shown to have distinct morphologic and genetic changes. Dysplastic morphology is also the primary diagnostic criterion in Papanicolaou smears, where microscope-based automated morphologic analysis is shown to be effective and approved by the Food and Drug Administration for primary screening.
Studies have indicated that cancer cells exhibit morphological characteristics that can be used to differentiate cancer cells from normal cells, however, most instruments capable of acquiring cellular images having enough detail to enable such morphological characteristics to be discerned do not have the throughput required to be able to detect very small numbers of cancer cells hidden in relatively large populations of normal cells. This problem is significant, because studies have indicated that the blood of a majority of patients who have had metastatic carcinomas contains fewer than one detectable carcinoma cell per 7.5 mL of blood, which is below the current threshold of five circulating tumor cells necessary to make a statistically robust diagnosis.
The above-noted commonly assigned related applications and issued patents disclose systems and apparatus for rapidly acquiring detailed cellular images from relatively large populations of cells. Using these detailed images, relatively small numbers of cancer cells present in a larger population can be statistically detected.
A common approach for detecting cancer cells seeks to reduce the effort required in manual microscopy-based analysis of a blood sample by eliminating or reducing the red blood cells and white blood cells in a sample being manually microscopically analyzed. The use of surface markers specific to cancer cells or specific to normal cells, as well as morphology and other features useful as a basis for reducing the population of white blood cells in a sample can be employed for this purpose. However, the procedure used to reduce the numbers of white blood cells in the sample that is to be manually analyzed may also substantially reduce the cancer cells in the sample, or may leave too many white blood cells in the sample. Typically, a person can realistically only manually review a few hundred cells in a session, since the manual analysis is visually tiring.
An alternative approach may be desirable that does not attempt to automatically analyze the images to directly identify cancerous cells. It would be desirable to reduce the effort required for manual review of images to detect cancerous or other types of abnormal cells. Thus, it would be desirable to derive a relatively small subset of images from all of the images that are automatically created, where the small subset of images are of objects that have not been automatically classified as normal components of blood, such as white blood cells. It would then be practical and efficient to manually review these images in the small subset to confirm whether the objects in the images are indeed cancer cells. Such an approach should increase the likelihood of identifying cancerous cells, by limiting the manual review to images of cells that may likely be abnormal.
In connection with such an approach, it would be desirable to develop a method for identifying specific features in images of white blood cells for use by an instrument to automatically classify each of the five types of white blood cells, so that the instrument can readily identify each type with an acceptable sensitivity (i.e., an acceptable percentage of false negative errors), and with an acceptable specificity (i.e., an acceptable percentage of false positive errors, which would result in wrongly classifying the type of a white blood cell). Sensitivity and specificity are discussed in greater detail below, in connection with FIG. 13. It would be desirable to then use these feature sets to define classifiers to enable an imaging system to automatically determine if a cell that is imaged is one of the five types of white cells, or instead, is an unidentified type of cell that may be a cancerous or other type of abnormal cell.